We consider maximum likelihood estimation with two or more datasets sampled from differ-ent populations with shared parameters.Although more datasets with shared parameters can increase statistical accuracy,this paper...We consider maximum likelihood estimation with two or more datasets sampled from differ-ent populations with shared parameters.Although more datasets with shared parameters can increase statistical accuracy,this paper shows how to handle heterogeneity among different populations for correctness of estimation and inference.Asymptotic distributions of maximum likelihood estimators are derived under either regulan cases where regularityconditions are satis-fled or some non-regular situations.A bootstrap variance estimator for assessing performance of estimators and/or making large sample inferenceis also introduced and evaluated ina simulation study.展开更多
基金Jun Shao’s research was partially supported by the National Natural Science Foundation of China[Grant Number 11831008]the U.S.National Science Foundation[Grant Number DMS-1914411].
文摘We consider maximum likelihood estimation with two or more datasets sampled from differ-ent populations with shared parameters.Although more datasets with shared parameters can increase statistical accuracy,this paper shows how to handle heterogeneity among different populations for correctness of estimation and inference.Asymptotic distributions of maximum likelihood estimators are derived under either regulan cases where regularityconditions are satis-fled or some non-regular situations.A bootstrap variance estimator for assessing performance of estimators and/or making large sample inferenceis also introduced and evaluated ina simulation study.
